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at an ambitious goal - designing secure and privacy-enhancing algorithms and framework for FL and applying our designs into real-world applications. To achieve this goal, we expect to deeply and
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unclear and how to design effective and efficient attack and defence algorithms are remain a challenge in the research community. Furthermore, most existing AML algorithms can only apply to Euclidean
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unclear and how to design effective and efficient attack and defence algorithms are remain a challenge in the research community. Furthermore, most existing AML algorithms can only apply to Euclidean
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. Researchers and students can explore privacy-preserving algorithms and technologies like federated learning and zero-knowledge proofs, enabling secure, distributed data processing. Self-Healing and Fault
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scenarios involving sensitive or confidential data. With the widespread adoption of ML algorithms for data analysis and decision-making, preserving the privacy of individuals' data has become a
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project, we aim to employ privacy-enhancing techniques to design new and efficient algorithms that can provide strong protection during the entire life cycle of ML models used on the devices
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stages are often asymptomatic or present with non-specific abdominal symptoms. Standalone clinical indicators like CA125 or traditional screening algorithms (e.g., ROMA) frequently produce ambiguous
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project, we aim to employ privacy-enhancing techniques to design new and efficient algorithms that can provide strong protection during the entire life cycle of ML models used on the devices
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stages are often asymptomatic or present with non-specific abdominal symptoms. Standalone clinical indicators like CA125 or traditional screening algorithms (e.g., ROMA) frequently produce ambiguous
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compact objects can be dug out of noisy time series because we understand what the signal should look like, and can therefore use simple algorithms based on matched-filter statistics. Detecting the